Advances in artificial intelligence and the use of big data are changing the way many large companies recruit for entry level and junior management positions. These days, graduates’ CVs may well have to impress an algorithm rather than an HR executive.
“There’s been a dramatic increase in the use of automation in [high] volume selection processes over the past two years,” says Sophie Meaney, managing director, client solutions and strategic development at Amberjack, which provides and advises on automated recruitment processes.
While algorithms supposedly treat each application equally, experts are divided about whether so-called robo-recruitment promises an end to human bias in the selection process — or whether it may in fact reinforce it.
“AI systems are not all equal,” says Loren Larsen, chief technology officer for HireVue, which has developed an automated video interview analysis system. It has been used by companies including Unilever, the consumer goods group, Vodafone, the telecoms company, and Urban Outfitters, the retailer. “I think you have to look [at] the science team behind the work,” says Mr Larsen.
“AI系统并非完全平等，”HireVue首席技术官洛伦?拉森(Loren Larsen)说。该公司开发出一套自动化的视频面试分析系统。包括消费品集团联合利华(Unilever)、电信运营商沃达丰(Vodafone)和零售商Urban Outfitters在内的很多公司已采用了该系统。“我认为，你必须考察一下这项工作背后的科学团队，”拉森说。
The problem, experts say, is that to find the best candidates an algorithm has first to be told what “good” looks like in any given organisation. Even if it is not fed criteria that seem discriminatory, an efficient machine-learning system will quickly be able to replicate the characteristics of existing workers. If an organisation has favoured white male graduates from prestigious universities, the algorithm will learn to select more of the same.
The growing reliance on automation to judge suitability for everything from a loan to a job or even to probation in the criminal justice system, worries Yuriy Brun, an associate professor specialising in software engineering at the University of Massachusetts.
从一笔贷款、一份工作，到刑事司法系统中的缓刑决定，在判断众多事情的合适性方面越来越依赖自动化，让马萨诸塞大学(University of Massachusetts)软件工程副教授尤里?布朗(Yuriy Brun)感到不安。
“A lot of the time a company will put out software but they don’t know if it is discriminatory,” he says. He points to the Compas tool in use in several US states to help assess a person’s likelihood to reoffend, which was reported to have discriminated against African Americans.
Prof Brun explains that, given the use of big data, algorithms will inevitably learn to discriminate. “People see that this is a really important problem. There’s a real danger of making things worse than they already are,” he says. His concern led him to co-develop a tool that tests systems for signs of bias.
Many of those working with robo-recruiters are more optimistic. Kate Glazebrook, chief executive of Applied, a hiring platform, says her mission is to encourage hiring managers to move away from what she calls “proxies for quality” — indicators such as schools or universities — and move to more evidence-based methods.
“In general, the more you can make the hiring process relevant, the more likely that you will get the right person for the job,” she says.
Applied anonymises tests that candidates complete online and feeds them, question by question, to human assessors. Every stage of the process has been designed to strip out bias.
With the same aim, Unilever decided in 2016 to switch to a more automated process for its graduate-level entry programme, which has about 300,000 applicants a year for 800 positions.
Unilever worked with Amberjack, HireVue and Pymetrics, another high volume recruitment company, which developed a game-based test in which candidates are scored on their ability to take risks and learn from mistakes, as well as on emotional intelligence.
Unilever says the process has increased the ethnic diversity of its shortlisted candidates and has been more successful at selecting candidates who will eventually be hired.
“The things that we can do right now are stunning, but not as stunning as we’re going to be able to do next year or the year after,” says Mr Larsen.
Still, robo-recruiters must be regularly tested in case bias has crept in, says Frida Polli, chief executive of Pymetrics. “The majority of algorithmic tools are most likely perpetuating bias. The good ones should have auditing.”